Space-time interaction in cancer cases arises when nearby cases occur at about the same time, and may be attributable to an infectious etiology or from exposures that cause a geographically localized increase in risk. But available techniques for detecting interaction do not account for residential mobility, nor do they evaluate sensitivity to induction and latency periods. This is an important problem for cancer, where latencies of a decade or more occur. Preliminary research evaluated the statistical behavior of the methods using simulated data to assess type I (false positives) and type II (false negatives) error, and applied the methods to 374 cases from an ongoing study of bladder cancer in 11 counties in southeastern Michigan. These preliminary studies demonstrated the ability of the methods to localize space-time interaction at the individual-level, and to accurately estimate the latency and induction periods used in the simulations. This project will build on these preliminary results to formally evaluate the feasibility of the new clustering techniques that account for residential mobility, latency and induction periods, relevant covariates (such as age) and risk factors (such as smoking). The Aims of the Phase I research are to: Aim 1. Create prototype software for visualization of residential mobility, calculation of the new clustering techniques, and visualization of the results. Aim 2. Evaluate the impacts of geocoding error on the new interaction statistics. Aim 3. Further validate the methods in an applied setting using the complete dataset from the study of bladder cancer in Michigan. This will use the additional information on cancer risk available from the case-control study to further validate the inferences drawn using the new case-only methods. Aim 4. Assess the availability of residential mobility data for routine use by cancer registries and cancer control professionals. In Phase II we will fully develop the software, establish educational tools targeting public health professionals, and conduct a series of training workshops to quickly disseminate knowledge and tools. PUBLIC HEALTH RELEVANCE: Residential history data are increasingly available, raising the possibility of routine surveillance in cancer registries and by cancer control professionals in a manner that accounts for individual mobility and that incorporates models of cancer latency and induction. These new techniques provide a mechanism for identifying those geographic locations and times associated with increases in cancer risk above and beyond that expected given covariates and risk factors in geographically mobile populations. The scientific and technologic innovations from this research are expected to revolutionize our ability to make sound cancer surveillance and control decisions based on an accurate understanding of space-time cancer incidence and mortality patterns, and premised on plausible cancer induction and latency periods. [unreadable] [unreadable] [unreadable] [unreadable]